A lot of CFOs I speak with say the same thing when AI comes up. They can see the long-term benefits, but they’re not overly chasing it either. Mostly, they’re trying to figure out how their teams can engage with AI without creating unintended consequences downstream.

The pressure from across the business is plain enough. Company boards are looking for productivity gains, and CEOs want faster forecasting and better insights. To help meet those expectations, people are already using AI tools like ChatGPT on the side, whether there’s a policy in place or not. What’s becoming clear is that when AI is already being used informally, standing still or staying neutral can actually increase risk for finance teams.

So, the question isn’t really “should we use AI in finance?” That ship has sailed. The question now is how AI can be genuinely useful, and not one more thing finance has to tidy up after the fact.

 

Why the CFO’s Office Can Feel Uneasy About AI

Unlike other business functions, finance has little tolerance for experimentation. When errors occur, they tend to surface in audits, control reviews, or regulatory conversations, often creating urgent accounting or cash management issues with no clear upside.

At the same time, AI is starting to earn its keep in some very ordinary parts of the finance function. The technology is great for sorting through large volumes of transactions, identifying anomalies and patterns that don’t look right, and helping with forecasting and scenario work without having teams reengineer models every time numbers change.

Where it works, it’s rarely flashy. It tends to sit within existing processes, surfacing issues earlier. McKinsey, for example, has pointed out that finance teams that are getting value from AI aren’t running side-desk experiments. They’re building it into core workflows like forecasting, cash management, and reporting, while keeping people firmly in the loop.

It’s also where agentic AI is starting to reshape how finance work gets done. Instead of supporting isolated tasks, agentic models operate across connected workflows, monitoring activity, surfacing exceptions, and intervening within defined controls.

In areas like B2B payments and cash management, that can translate directly into earlier detection of fraud signals, stronger control over payment execution, and more accurate forecasting inputs feeding into CFO-level decision making.

 

Why ‘Generic AI’ Falls Short

For all of those reasons, a divide is emerging between generic AI tools and enterprise-grade, finance-native AI, with different tasks favouring different tools.

As for generic AI, it operates outside of financial systems without visibility into which numbers are final, which are provisional, how controls are applied, or how workflows are structured. That creates a gap between speed and trust.

Finance-native AI and agentic platforms, by contrast, works inside governed environments and is context-aware. It’s aligned to payment flows, reconciled cash positions, approval hierarchies, and audit requirements. Every output is grounded in traceable data and proven controls.

The difference is operational and technical. Generic AI can respond quickly, but finance-grade AI produces outputs that stand up to audit, board scrutiny, and regulatory review.

Think of it this way: when someone asks, “Why did we think this?” enterprise AI can show its work. Generic AI often cannot.

 

How Finance-native AI Makes a Difference

CFOs who are most comfortable with where things are heading tend to be the ones drawing boundaries today that will impact tomorrow. They’re not trying to apply AI everywhere. Their focus is on using it for specific problems where AI genuinely helps.

They know who owns the output, regardless of the tools used to support it. And those owners should expect results to be questioned, not blindly accepted.

But that sceptical approach isn’t about slowing things down for the sake of it; it’s about applying the same discipline finance brings to everything in its remit across the business.

Speaking of business impacts, much of the early conversation around AI has focused on operational efficiency. That emphasis is shifting, with impact becoming more visible at the CFO level.

When AI is applied in the right places (inside payments, cash visibility, and working capital processes), it begins to shift core financial outcomes, including:

  • Better cash positioning through more accurate, real-time visibility and forecasting
  • Improved working capital performance driven by faster reconciliation and optimised payables/receivables timing
  • Reduced fraud exposure through earlier detection and intervention in payment workflows
  • Faster, more confident board-level reporting, with outputs that are explainable, traceable, and audit-ready

These are not incremental gains. They shape liquidity, risk posture, and strategic decision-making.

 

Embedding AI Where It Works Best

It’s understandable that CFOs are starting AI projects with the systems they already use and trust on a daily basis. Instead of bolting on AI, they want it built into their payments, cash visibility, AP and AR processes, and exception handling.

They want it in the very places where their data is governed, where workflows are familiar, and where the rules are already in place and clear.

Purpose-built CFO solutions make a difference. By that, I mean using AI that’s designed to be connected to finance systems. It’s working with known data, inside already-established processes. It’s using embedded AI to help surface issues earlier, reduce manual effort, and fit into how finance teams actually operate day-to-day.

Critically, this is also where outcomes become measurable, not just in time saved, but in risk reduced, cash optimised, and decisions made with greater confidence.

 

The Role of Data

One of the biggest shifts happening right now is how CFOs are thinking about data in the context of AI. This is a rapidly progressing study in how best to combine powerful assets.

It’s important to note that AI in finance is only as reliable as the data it ingests. Unlike other parts of the business, finance depends on structured, governed, and traceable information, where every number has a source and every decision can be explained.

The challenge is that many organisations are trying to apply AI on top of fragmented or inconsistently defined financial data. In that scenario, issues don’t just persist, they scale faster.

When that same data is spread across systems, manually adjusted, or inconsistently defined, the knock-on effect becomes harder to ignore. Patterns are harder to trust, anomalies lose context, and outputs become difficult to stand behind in an audit or board discussion.

For CFOs, that quickly erodes confidence, regardless of how advanced the technology appears. That’s why the real value of AI comes from applying it to high-quality, governed financial data inside established workflows.

As this space develops, the advantage will increasingly go to organisations that treat data not as a byproduct of finance processes, but as the foundation for AI-driven decision making.

When AI is working with trusted payment data, reconciled cash positions, and consistent transaction histories, it can surface insights earlier, flag risks more accurately, and support decisions with greater confidence. In that context, clean data is the fuel that determines whether AI produces noise or meaningful, actionable intelligence.

For finance teams, getting the data foundation right isn’t just part of the AI journey. It is what separates experimentation from enterprise value.

 

On Being Deliberate

Ultimately, the question isn’t whether to rush ahead or to sit back and wait. It’s about being deliberate. Focusing on the problems that genuinely call for it, and being clear where AI adds value versus where it complicates things.

Increasingly, it comes down to choosing AI that aligns with how finance actually operates: controlled, contextual, and accountable.

Most importantly, it’s about embedding AI into areas of finance where discipline already exists, rather than relying on ad hoc, well‑intended usage.